ARCHIVAL REPORT
A Thalamocorticostriatal Dopamine Network for
Psychostimulant-Enhanced Human Cognitive
Flexibility
Gregory R. Samanez-Larkin, Joshua W. Buckholtz, Ronald L. Cowan, Neil D. Woodward,
Rui Li, M. Sib Ansari, Catherine M. Arrington, Ronald M. Baldwin, Clarence E. Smith,
Michael T. Treadwa, Robert M. Kessler, and David H. Zald
Background: Everyday life demands continuous flexibility in thought and behavior. We examined whether individual differences in
dopamine function are related to variability in the effects of amphetamine on one aspect of flexibility: task switching.
Methods: Forty healthy human participants performed a task-switching paradigm following placebo and oral amphetamine
administration. [18F]fallypride was used to measure D2/D3 baseline receptor availability and amphetamine-stimulated dopamine release.
Results: The majority of the participants showed amphetamine-induced benefits through reductions in switch costs. However, such
benefits were variable. Individuals with higher baseline thalamic and cortical receptor availability and striatal dopamine release showed
greater reductions in switch costs following amphetamine than individuals with lower levels. The relationship between dopamine
receptors and stimulant-enhanced flexibility was partially mediated by striatal dopamine release.
Conclusions: These data indicate that the impact of the psychostimulant on cognitive flexibility is influenced by the status of
dopamine within a thalamocorticostriatal network. Beyond demonstrating a link between this dopaminergic network and the
enhancement in task switching, these neural measures accounted for unique variance in predicting the psychostimulant-induced
cognitive enhancement. These results suggest that there may be measurable aspects of variability in the dopamine system that
predispose certain individuals to benefit from and hence use psychostimulants for cognitive enhancement.
Key Words: Amphetamine, cognitive control, dopamine, flexibility,
parietal cortex, PET, prefrontal cortex, striatum, thalamus
C
ognitive flexibility refers to the broad set of skills used in
everyday life to adjust behavior according to the changing
demands of the environment. These skills have been
associated with corticostriatal brain regions (1–8) modulated by
the biogenic amines dopamine (DA), serotonin, and norepinephrine (9,10). The ability to switch tasks represents an important
component of cognitive flexibility because it is essential for
adaptively adjusting behavior in response to changing internal or
external needs. This ability can be quantified as the additional
amount of time it takes to switch to a new task relative to
repeating the same task (“switch cost”) (11). Deficits in this ability
arise in a number of neuropsychiatric disorders including
From the Department of Psychology (GRS-L, DHZ) and Institute of
Imaging Science (GRS-L), Vanderbilt University, Nashville, Tennessee;
Department of Psychology (JWB), Harvard University, Cambridge,
Massachusetts; Departments of Psychiatry (RLC, NDW, DHZ) and
Radiology and Radiological Sciences (RL, MSA, RMK), Vanderbilt
University, Nashville, Tennessee; Department of Psychology (CMA),
Lehigh University, Bethlehem, Pennsylvania; Molecular Neuroimaging
(RMB), New Haven, Connecticut; DXP Imaging (CES), Norton Neuroscience Institute, Louisville, Kentucky; and Department of Psychiatry
(MTT), McLean Hospital/Harvard Medical School, Belmont,
Massachusetts.
Address correspondence to Gregory R. Samanez-Larkin, Ph.D., Psychological Sciences, Vanderbilt University, 111 21st Avenue South, Nashville,
TN 37240-7817; E-mail: [email protected].
Received Feb 15, 2012; revised and accepted Oct 31, 2012.
0006-3223/$36.00
http://dx.doi.org/10.1016/j.biopsych.2012.10.032
attention-deficit/hyperactivity disorder (12) and Parkinson’s disease (13), as well as in normal aging (14).
Task switching has both theoretically (15) and empirically
been associated with the neuromodulator DA. Although manipulations of the DA system affect switch costs (12,13,16–18),
responses to such manipulations are variable across participants.
Understanding such variability is critical for evaluating the utility
of agents aimed at modulating the DA system in a given
individual. This issue takes on particular importance for psychostimulants given their widespread licit and illicit usage for
promoting attention and alertness (19).
Genetic studies suggest that heritable individual variability in
the function or expression of DA signaling pathway components,
including catechol-O-methyltransferase, the DA transporter, and
D2 receptors may contribute to individual differences in switch
costs (20–23). However, to date these genetic studies have not
directly measured DA functioning; thus, the relationship between
variability in DA and individual differences in behavior are
inferred rather than empirically observed. Indeed, knowledge of
the cognitive correlates of individual differences in DA functioning of specific brain regions is largely absent from the human
literature because cognitive genetic association studies are
typically unable to provide information about regional specificity,
and most DA positron emission tomography (PET) imaging
studies use low-affinity radioligands (e.g., [11C]raclopride) that
are only suitable for assessing binding potential in the striatum.
In this study, we examined individual differences in psychostimulant enhancement of cognitive flexibility, specifically focusing
on task switching (11), by combining pharmacologic manipulation
of the DA system with PET imaging of DA D2/D3 receptors in
healthy young adults. Given previous studies suggesting that the
ability of DA agonists to enhance task-switching behavior may
depend on interindividual variation in DA networks (20), we
examined whether measures of receptor availability and
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psychostimulant-induced DA release predict individual differences in
the cognitive benefits of amphetamine (d-AMPH) administration.
Methods and Materials
Participants
Forty neurologically and psychiatrically healthy right-handed
adult human participants (mean age ¼ 22.4, range 18–33; 21
men, 19 women) with estimated IQ greater than 80 and no
history of substance abuse were studied as part of an ongoing
investigation of individual differences in striatal and extrastriatal
DA release. All participants provided written informed consent
approved by the Vanderbilt University Institutional Review Board.
Female participants were studied during the early follicular phase
of their menstrual cycle. Full screening and study eligibility details
are provided in Supplement 1.
Task Switching
All participants completed a classic task-switching paradigm
(24) (Figure S1 in Supplement 1). The task included predictable
switches on every other trial (see Methods in Supplement 1 for
more details). There were 352 trials per session. Switch costs were
calculated as the difference between the average reaction time
on switch trials (magnitude to odd–even; odd–even to magnitude) and the average reaction time on repetition trials (odd–even to
odd–even; magnitude to magnitude) collapsing across two responseto-stimulus intervals (see Results in Supplement 1 for details).
Participants completed one round of the switch task (352
trials) on placebo and a second round (352 trials) on a .43 mg/kg
oral dose of d-AMPH. Participants performed the task approximately 90 minutes after ingesting the drug and before entering
the PET scanner. This time delay was selected as the likely peak
time for observing behavioral effects of the drug. We selected dAMPH as the pharmacologic agent because of its widespread use
(both licit and illicit) as a cognitive enhancer and its ability to
stimulate DA release in a manner that reliably displaces our
radiotracer, [18F]fallypride. It is this latter property that allows us
to measure individual differences in DA responses to d-AMPH in
both striatal and extrastriatal regions (25). The sessions were
separated by an average of 18.5 days (SD ¼ 19.8, range ¼ 1–85).
Number of days between sessions was uncorrelated with
changes from placebo to d-AMPH for both mean reaction time,
r ¼ .08, p ¼ .61, and switch cost, r ¼ .05, p ¼ .77. Basic speed of
processing and motor measures were also collected during both
the placebo and d-AMPH sessions and included digit symbol
coding, symbol search, finger tapping, and toe tapping to ensure
that observations were specific to task switching and did not
reflect simple psychomotor processing speed. A subset of 10 of
these participants completed two rounds of the task (separated
by the same number of days as their original two sessions)
several years later to estimate the effect of task repetition in the
absence of a drug (see Results in Supplement 1). This follow-up
was conducted 4–5 years after the tasks were completed the first
time, so it is unlikely that any initial exposure to the task carried
over across this interval. An additional, separate sample of 10
healthy young adults (aged 21–30) performed the task twice but
did not receive d-AMPH or undergo PET imaging (see Results in
Supplement 1).
PET Acquisition and Preprocessing
All PET images were acquired using [18F]fallypride. Unlike
other D2/D3 ligands, [18F]fallypride allows stable estimates of D2/
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G.R. Samanez-Larkin et al.
D3 binding in both striatal and extrastriatal regions (26,27) with
high test–retest reliability (see Methods in Supplement 1 for
more details). Protocols for PET image acquisition and analysis
were derived from a larger ongoing study and have been
previously published (26,28,29).
Participants received two PET scans using [18F]fallypride. The
first scan was a baseline placebo scan; the second scan was
performed while the participant received a d-AMPH challenge.
PET imaging was performed on a GE Discovery LS scanner (GE,
Milwaukee, Wisconsin) located at Vanderbilt University Medical
Center that was upgraded to a Discovery STE system (GE) during
the course of the study (10 participants on LS, 30 participants on
STE). All participants received their baseline and d-AMPH scans
on the same scanner. Prior analyses with the same data set reveal
no significant differences between scanners (28). Nevertheless,
scanner was included here as a covariate in all analyses.
Following reconstruction both scanners had similar in-plane
and through-plane resolution. [18F]fallypride was produced in
the radiochemistry laboratory attached to the PET unit, following
synthesis and quality control procedures described in U.S. Food
and Drug Administration Investigational New Drug application
(30). Scans were timed to start 3 hours after .43 mg/kg oral dAMPH administration, which was timed to coincide with the
period of peak plasma d-AMPH. 3-D emission acquisition scans
were performed following a 5.0 mCi slow bolus injection of
[18F]fallypride (specific activity ⬎3,000 Ci/mmol). Serial scans
were started simultaneously with the bolus injection of [18F]fallypride and were obtained for approximately 3.5 hours, with two
15-minute breaks for participant comfort. Computed tomography
transmission scans were collected for attenuation correction
before each of the three emission scans.
Each participant’s serial PET scans were first corrected for
motion across scanning periods and then coregistered to the
participant’s structural T1-weighted magnetic resonance (MR)
image (see Supplement 1 for MR imaging details). Regional D2/
D3 binding potential, nondisplaceable (BPND) was calculated on a
voxelwise basis using the full reference region method (31), with
cerebellum chosen as the reference region because of its relative
lack of D2/D3 receptors (32). Using the full reference region
method, we have shown near-perfect correlation (r ¼ .99) with
modeled estimates using a metabolite-corrected plasma input
function (33). Although this approach is slower computationally
than the simplified (three-parameter) tissue reference method, it
robustly estimates the key variable of interest (binding potential),
and we have observed excellent convergence of modeled fits in
regions with both high and low DA D2/D3 receptor levels.
Voxelwise kinetic modeling was executed using Interactive Data
Language. Individual voxelwise images of percent-change in
[18F]fallypride binding from placebo to d-AMPH (representing
percent-change in DA release) were created by subtracting each
participant’s d-AMPH scan from their placebo scan and dividing
the resulting imaging by the placebo scan.
Individual Difference Analyses
Before the group analyses, a composite PET binding potential/
T1-weighted MR image was created for each participant and
warped to Montreal Neurological Institute space. The transformation matrix from this warping was then applied to the binding
potential maps to bring all participants’ data into a common
space. Individual difference analyses of the PET data were
performed in SPM8 (http://www.fil.ion.ucl.ac.uk/spm/) by separately regressing participants’ switch cost scores against their D2/
D3 binding (placebo) and d-AMPH-induced DA release (percent-
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d-AMPH Switch Cost (msec)
G.R. Samanez-Larkin et al.
Results
600
450
300
150
0
0
150
300
450
600
Placebo Switch Cost (msec)
Figure 1. The behavioral measure of inflexibility (switch cost) was
reduced with an oral dose of amphetamine (d-AMPH) for most participants. Gray line is unity line (placebo switch cost ¼ d-AMPH switch cost).
N ¼ 40.
change) images. In all cases, age, sex, and scanner were included
as covariates. Statistical parametric maps were thresholded at a
height of t ⬎ 3; cluster threshold was set to 30 contiguous
voxels. Only voxels within clusters surviving a cluster extent
correction for multiple comparisons (pFDR ⬍ .05) at this threshold
are reported. The topologic false discovery rate default in SPM8
was used here, but the results are unchanged if the correction is
changed to family-wise error rate.
Structural Equation Modeling
A structural equation model was used to illustrate the
relationships between DA binding potential and release across
a thalamic, cortical, and striatal network and their influence on
d-AMPH-induced cognitive flexibility. For the latent variable, one
of three paths to the manifest variables was fixed to an
unstandardized coefficient of 1 during model fitting.
Behavioral Results
The majority of participants showed a behavioral benefit of
d-AMPH. In addition to an overall decrease in mean reaction time
under d-AMPH (744 msec, SD ¼ 157) compared with placebo
(837 msec, SD ¼ 196), mean switch costs were also reduced
under d-AMPH (210 msec, SD ¼ 101) compared with placebo
(299 msec, SD ¼ 115). The average reduction in switch cost from
placebo to d-AMPH (placebo – d-AMPH ¼ 89 msec, SD ¼ 89)
was significant across the sample, t(39) ¼ 6.32, p ⬍ .0001. For
comparison of switch costs under placebo and d-AMPH, see
Figure 1. Additionally, the magnitude of the d-AMPH increase
in cognitive flexibility was predicted by baseline switch costs,
r ¼ .53, p ⬍ .001, such that individuals with larger switch costs
under placebo showed greater behavior change.
Neuroimaging Results
The behavioral increase in flexibility (i.e., average reduction in
switch cost from placebo to d-AMPH) was also associated with
baseline DA D2/D3 receptor availability (as indexed by [18F]fallypride binding potential, nondisplaceable: BPND) in the lateral
prefrontal cortex, left thalamus, and right inferior parietal lobule
(p ⬍ .05 whole-brain corrected). In each region, higher levels of
BPND were associated with larger d-AMPH-associated cognitive
benefits (see Figure 2; Table S1 in Supplement 1). Increases in
flexibility were also associated with DA release in the striatum
(p ⬍ .05 whole-brain corrected) as indexed by the change in
[18F]fallypride BPND following d-AMPH. Higher levels of DA release
in anteromedial aspects of the caudate head were associated with
larger cognitive benefits (Figure 3; Table S1 in Supplement 1).
Although this cluster appears to extend into the white matter
immediately anterior to the caudate, note that the spatial resolution of the PET data is much lower than the MR imaging template
used as the underlay in the figures. No regions showed effects in
d-AMPH SC ∆ (msec)
R
R
350
225
100
–25
–150
0
R
1
Prefrontal BPND
p < 0.001 0.005 0.01
0.05
Figure 2. Increases in cognitive flexibility (reductions in switch cost) were associated with dopamine (DA) D2/D3 binding potential in the (A) lateral frontal cortex,
(B) thalamus, and (C) parietal cortex. Scatterplot y axis is size of switch cost reduction from placebo to amphetamine in milliseconds
(d-AMPH SC D). Scatterplot is displayed only as a sample depiction of effects to illustrate the absence of outliers. N ¼ 40. BPND, binding potential,
nondisplaceable; R, right.
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R
p < 0.001 0.005 0.01
R
d-AMPH SC ∆ (msec)
G.R. Samanez-Larkin et al.
350
225
100
–25
–150
–40
0
40
Caudate DA release
0.05
Figure 3. Increases in cognitive flexibility (reductions in switch cost) were associated with dopamine (DA) release in the caudate. Scatterplot y axis is size
of switch cost reduction from placebo to amphetamine in milliseconds (d-AMPH SC D). Scatterplot is displayed only as a sample depiction of effects to
illustrate the absence of outliers. N ¼ 40.
the opposite direction (i.e., negative correlation between BPND
and cognitive benefit) at the whole-brain threshold. There were
also no regions that were significantly associated with switch
cost at baseline (placebo) at the cluster-corrected whole-brain
threshold.
The associations between DA receptors and release in these
regions and increased flexibility were specific and not simply a
general cognitive or motor benefit. The relationships with baseline BPND and d-AMPH-induced release in these regions remained
significantly associated with the d-AMPH behavioral benefit after
controlling for performance on digit symbol coding, symbol
search, finger tapping, or toe tapping tasks. The d-AMPH
improvement in performance was significant for digit symbol
coding, t(38) ¼ 3.06, p ⬍ .005, and symbol search, t(39) ¼ 8.20,
p ⬍ .001, but nonsignificant for finger tapping, t(39) ¼ 1.68, p ¼
.10, and toe tapping, t(39) ¼ 1.53, p ¼ .13. Importantly, these
basic measures of motor change post d-AMPH were not
associated with changes in switch costs (all 9r9 ⬍ .19, p ⬎ .26).
Follow-up analyses also revealed that motor change measures
were not significantly associated with prefrontal cortical BPND (all
9r9 ⬍ .29, p ⬎ .08), parietal cortical BPND (all 9r9 ⬍ .26, p ⬎ .12),
thalamic BPND (all 9r9 ⬍ .26, p ⬎ .13), or caudate DA release (all
9r9 ⬍ .15, p ⬎ .36). Follow-up quadratic (inverted-U) effects were
tested with data extracted from regions of interest in the lateral
prefrontal cortex, parietal cortex, thalamus, and caudate. These
measures of DA receptors (cortex, thalamus) and release (caudate)
for each regions of interest were average BPND values across each
cluster identified in the whole-brain analyses examining individual
differences in the drug effect. All relationships were linear, with no
evidence of an additional inverted-U relationship between the
neural measures and behavior change in any of these regions.
Stepwise regression analyses examined whether the neural
measures explained additional variance in predicting behavioral
benefits of d-AMPH over baseline performance levels alone
(Table 1). As reported earlier, in the first step placebo switch
costs (i.e., baseline performance) explained a significant amount
of variance in the d-AMPH behavioral benefit. For step two, a
composite of thalamic-cortical BPND was created by averaging
the z-scored measures of BPND across the thalamus, lateral frontal
cortex, and parietal cortex (BPND measures in these three regions
were highly correlated consistent with past analyses (26); all
rs ⬎ .58, p ⬍ .0001). In step two, significantly more variance in
behavior change was explained by the addition of thalamiccortical BPND (R2 change ¼ .29), F(1,34) = 23.59, p ⬍ .0001. In step
three, significantly more variance was explained by adding
caudate DA release to the model from step two (R2 change =
.06), F(1,33) = 5.31, p ⬍ .05. Importantly, the behavioral measure
of the drug benefit used in this regression model is based on a
subtraction from the baseline behavioral measure. Thus, the
evidence that the size of the d-AMPH-induced reduction in
switch cost is partially explained by the baseline switch cost is
not surprising. However, this analysis demonstrates that the
subcortical and cortical PET measures explain variance above
and beyond that provided by baseline behavioral performance
alone. Note that we use the term “thalamic-cortical” to refer to
this correlated network of thalamic and cortical regions where
Table 1. Behavioral and Neural Predictors of Amphetamine-Induced Cognitive Flexibility
d-AMPH Switch Cost Reduction
Placebo Switch Costa
Thalamic-Cortical DA BPNDa
Caudate DA Releasea
Agea
Sexa
Scannera
R2
Adjusted R2
Observations
Step 1
Step 2
Step 3
.52 (.14); 3.64b
—
—
–.07 (.15); –.49
–.03 (.14); –.21
.05 (.15); .34
.29c
.21c
40
.30 (.12); 2.51c
.76 (.16); 4.86b
—
.17 (.13); 1.31
–.13 (.11); –1.12
.25 (.12); 1.97
.58b
.52b
40
.26 (.12); 2.29c
.54 (.18); 3.10d
.30 (.13); 2.30c
.09 (.12); .74
–.11 (.11); –1.02
.21 (.12); 1.80
.64b
.57b
40
BPND, binding potential, nondisplaceable; d-AMPH, amphetamine; DA, dopamine.
Coefficient (SEM); t statistic.
b
p ⬍ .001.
c
p ⬍ .05.
d
p ⬍ .01.
a
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Caudate
Release
e
e
Prefrontal BP
e
0.92
0.44
0.51
0.86
e
ic -
a
0.38
Flexibility
l
a
m
BP
ND
Th al
0.70
Parietal BP
C o rti c
d-AMPH SC ∆
e
Figure 4. Structural equation model of
the thalamocorticostriatal network effects
on psychostimulant-enhanced cognitive
flexibility. Path coefficients are standardized regression betas. The path between
the thalamic-cortical latent variable and
the thalamus manifest variable was fixed
to an unstandardized coefficient of 1
during estimation. All paths are significant
at p ⬍ .05. Lowercase encircled “e”
indicates residual error variance terms.
d-AMPH SC D ¼ switch cost reduction
from placebo to amphetamine (expressed
as an increase in flexibility). BPND, binding
potential, nondisplaceable.
Thalamus BP
receptor availability is associated with the effect of d-AMPH. We
are not using the term to refer to the glutamatergic thalamocortical projections from thalamus to cortex.
In the full model (step three) thalamic-cortical BPND and
caudate DA release were significant predictors of increased
flexibility (Table 1), suggesting that they each contribute at least
some unique variance. A structural equation model was used to
formalize both the relationships between these regions and their
unique effects on behavior change. A model in which thalamiccortical BPND (represented as a single latent variable indexed by
thalamic, frontal, and parietal BPND) and caudate DA release are
significantly correlated but also uniquely predict psychostimulant-enhanced cognitive flexibility provided a good fit to
the data (w24 ¼ 4.69, p ¼ .32; confirmatory fit index ¼ .99, root
mean square error of approximation ¼ .066). The model is
displayed in Figure 4 with path coefficients. All paths in the
model are significant at p ⬍ .05. Although both thalamic-cortical
BPND and striatal DA release are significantly associated with
behavior change, the model also demonstrates a partial mediation effect such that the influence of thalamic-cortical BPND on
stimulant-enhanced flexibility is partially mediated by the impact
of thalamic-cortical BPND on DA release in the striatum (Sobel
test ¼ 2.20, p ⬍ .05).
Discussion
This study indicates that the psychostimulant d-AMPH generally enhances task switching in healthy young adults, with
improvements exceeding those that can be explained by simple
motor effects alone. The overall enhancement in task switching is
consistent with both theoretical accounts of a role for DA in
allowing switching of behavioral outputs (15) and prior evidence
that levodopa and methylphenidate reduce switch costs (12,13)
in patient populations. However, to our knowledge, this is the
first study to demonstrate the beneficial effect of d-AMPH on
switch costs in healthy human participants. As expected, there
were significant individual differences in the degree to which dAMPH reduced switch costs. Critically, the extent to which
participants showed improvement in cognitive flexibility following d-AMPH was associated with individual differences in a
dopaminergic network of cortical and subcortical brain regions.
Improvements in performance from placebo to d-AMPH were
predicted by D2/D3 receptor availability in the lateral frontal and
parietal cortices and thalamus. The localization of these relationships in lateral frontal and parietal regions is consistent with
lesion studies and functional and structural imaging literature on
task switching (3–5,7,8,22). The evidence that individuals with a
greater number of available (unoccupied) D2/D3 receptors show
larger benefits of d-AMPH for task switching suggests that having
more sites for DA to act in the thalamus and cortex aids in its
enhancement of cognitive function.
The effects of cortical D2 engagement may be understood
within existing models of prefrontal DA action (34). According to
this model, D1 and D2 receptors alter the response properties of
prefrontal neurons, such that D1 receptors enhance the maintenance of information in working memory buffers by suppressing the ability of distracters to engage local circuitry, whereas D2
receptors lower the barriers for new or different information to
gain access to these buffers, promoting flexibility. Updating has
been shown to be linked more strongly to D2 receptor stimulation than D1 receptor stimulation (35,36). In the context of task
switching, enhancing D2 signaling may improve task switching
by facilitating the accessibility of new information to working
memory, thus preventing unnecessarily long maintenance of the
prior task rule.
It is likely that any enhanced signaling here is occurring within
an optimal range because excessive D2 signaling will degrade
attentional performance by promoting distractibility (36,37). It
should also be noted that d-AMPH will have an impact on D1
receptors. The D2-sensitive ligand used in our study does not
provide evidence about the relative D1 and D2 dominance in the
cortex. Although there is strong pharmacologic and genetic
evidence for a selective relationship between D2 receptor
function and flexibility (20), we did not measure D1 receptors
here, and, at present, data linking PET measures to these specific
state conditions is lacking.
Critically, a positive relationship was also found between DA
release in the caudate and the psychostimulant enhancement of
task switching. It has been suggested that striatal DA may play a
more central role than cortical DA in flexibility (38). Striatal DA
has been shown to influence the neural efficiency of other
dorsolateral striatal and prefrontal cortical regions, which directly
influence switching behavior. Striatal DA may also serve a gating
function on cortical connections (39,40), which may aid in the
continuous alternation of responses required by the task.
Although we found specific associations between DA receptors and release and the cognitive benefit of d-AMPH, it is
important to acknowledge that the effects of these stimulants are
not limited to the DA system. d-AMPH and methylphenidate also
act on the norepinephrine transporter. Thus, this behavioral
benefit is also likely to be partially dependent on other neurochemical systems (10,41). Nevertheless, a significant portion of
the variance in behavior change was accounted for by individual
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differences in DA binding potential and release (adjusted R2
range was .20–.38 across individual regions). We did not assess
other neurochemical systems here, so it is not possible to
examine potential relationships between behavior change and
variability in the norepinephrine system or directly compare the
size of the effects between neuromodulators.
Although previous studies have examined relationships
between DA drug effects on other flexibility measures (e.g.,
reversal learning) and striatal DA function (42,43), the goal of
the whole-brain approach in this study was to better characterize
the role of a broader DA network. A structural equation model
was used to test the hypothesized associations between these
cortical and subcortical effects and their relationship with
psychostimulant-enhanced flexibility. The model supports the
hypothesis that these regions compose a unified network but
also make independent contributions to behavior change. The set
of regions in this network is remarkably consistent with documented thalamocorticostriatal circuits (44,45). Although thalamic
activation has been reported in studies of task switching (1,2,8), it
is rarely discussed. The results of our study support an important
role for the thalamus in this dopaminergic network (44,46–48). In
previous discussions of the role of corticostriatal circuits in
switching, there is an implicit assumption that the loops proceed
through thalamic relays. However, the observation that D2/D3
binding potential in the thalamus influences the results suggests
that there could be a more explicit influence of the thalamus
that extends beyond an automatic relay. As such, we adopt the
term “thalamocorticostriatal” in describing this network. Of
course, a complete circuit model of these thalamic, cortical, and
striatal regions necessarily includes gamma-aminobutyric acid–
ergic and glutamatergic pathways that we did not measure here.
Our use of the “network” terminology refers to the correlated DA
receptor and release effects in thalamic, cortical, and striatal
regions.
The results also demonstrate a partial mediation effect such
that the thalamic and cortical receptor influence on stimulantenhanced flexibility is partially mediated by striatal DA release.
The relationship between thalamic and cortical binding potential
and striatal DA release is specified directionally for two reasons.
First, the measure of DA release that we use in this study is
d-AMPH induced and depends on baseline levels of DA receptors.
Second, directionality is anatomically predicted by well-known
dorsolateral prefrontal loops that include direct pathways from
the prefrontal cortex to the striatum. Although we model
directionality, more generally, it is possible that this relationship
could be bidirectional. Although previous studies and theory
have emphasized the opponent roles of the striatum and
prefrontal cortex for supporting flexibility and stability, respectively (36), our results suggest that both striatal and cortical
regions may be serving complementary functions.
Some limitations in the study should be noted. The order of
administration of placebo and drug was not counterbalanced.
Although two sets of additional behavioral data suggest that the
drug effects are larger than the task repetition effects (Results in
Supplement 1), we cannot completely rule out the possibility that
a portion of the variance in performance across participants may
also be related to individual differences in learning ability (42,43).
Future studies will need to collect measures of both flexibility
and learning to formally examine these potential relationships.
Also, the present sample was composed entirely of healthy
young adults. It is not clear whether the same relationships
would be observed in a clinical sample (49,50). Another important limitation is that the behavioral change was only examined
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G.R. Samanez-Larkin et al.
over a short time scale from a single dose of d-AMPH. We did not
measure repeated exposure to the stimulant. It is possible that
the flexibility benefits may plateau over time or even reverse with
sensitization (51). A related limitation of using a single dose of
d-AMPH in this study is that we cannot establish dose–response
curves with the available data.
An increasing number of otherwise healthy adults use psychostimulants for cognitive enhancement (19). Despite the potential for
addiction (52), individuals will continue to use d-AMPH and other
psychostimulants to manipulate their attention and arousal (53,54).
Our results suggest that there may be measurable aspects of
variability in the DA system that predispose certain individuals to
benefit from and hence use psychostimulants for cognitive
enhancement. These factors may also influence the risk for abusing
such medications. In a recent study, we found that increased DA
release in the striatum was associated with increased self-reported
desire for more d-AMPH (28). Although we did not specifically
measure the perceived cognitive benefits here, it is possible that
such perceived cognitive effects of initial stimulant exposure may
be an important part of the subjective experience that contributes
to the later development of addiction (55).
GRS-L was supported by National Institute of Mental Health
Training Grant No. T32-MH018921, a National Institute on Aging
postdoctoral fellowship (National Research Service Award F32AG039131), and a National Institute on Aging Pathway to Independence Award (Grant No. K99-AG042596) during data analysis
and manuscript preparation. This research was funded by National
Institute on Drug Abuse (Grant No. R01-DA019670) to DHZ and was
supported by Clinical and Translational Science Award Award No.
UL1TR000445 from the National Center for Advancing Translational
Science. The contents of this article are solely the responsibility of
the authors and do not necessarily represent official views of the
National Center for Advancing Translational Science or National
Institutes of Health.
Thanks to Evan Shelby and Ashley Schwartzman for assistance
with data collection and to Chrystyna Kouros and Lauren Atlas for
advice on structural equation modeling.
All authors report no biomedical financial interest or potential
conflicts of interest.
Supplementary material cited in this article is available online.
1. Kimberg DY, Aguirre GK, D’Esposito M (2000): Modulation of taskrelated neural activity in task-switching: An fMRI study. Brain Res Cogn
Brain Res 10:189–196.
2. Sohn MH, Ursu S, Anderson JR, Stenger VA, Carter CS (2000): The role
of prefrontal cortex and posterior parietal cortex in task switching.
PNAS 97:13448–13453.
3. Aron AR, Monsell S, Sahakian BJ, Robbins TW (2004): A componential
analysis of task-switching deficits associated with lesions of left and
right frontal cortex. Brain 127:1561–1573.
4. Cools R, Ivry RB, D’Esposito M (2006): The human striatum is necessary
for responding to changes in stimulus relevance. J Cogn Neurosci 18:
1973–1983.
5. Badre D, Wagner AD (2006): Computational and neurobiological
mechanisms underlying cognitive flexibility. PNAS 103:7186–7191.
6. Esterman M, Chiu Y-C, Tamber-Rosenau BJ, Yantis S (2009): Decoding
cognitive control in human parietal cortex. PNAS 106:17974–17979.
7. Gold BT, Powell DK, Xuan L, Jicha GA, Smith CD (2010): Age-related
slowing of task switching is associated with decreased integrity of
frontoparietal white matter. Neurobiol Aging 31:512–522.
8. Kim C, Johnson NF, Cilles SE, Gold BT (2011): Common and distinct
mechanisms of cognitive flexibility in prefrontal cortex. J Neurosci 31:
4771–4779.
G.R. Samanez-Larkin et al.
9. Robbins TW (2007): Shifting and stopping: Fronto-striatal substrates,
neurochemical modulation and clinical implications. Philos Trans R Soc
Lond B Biol Sci 362:917–932.
10. Kehagia AA, Murray GK, Robbins TW (2010): Learning and cognitive
flexibility: Frontostriatal function and monoaminergic modulation.
Curr Opin Neurobiol 20:199–204.
11. Monsell S (2003): Task switching. Trends Cogn Sci 7:134–140.
12. Kramer AF, Cepeda NJ, Cepeda ML (2001): Methylphenidate effects on
task-switching performance in attention-deficit/hyperactivity disorder.
J Am Acad Child Adolesc Psychiatry 40:1277–1284.
13. Cools R, Barker RA, Sahakian BJ, Robbins TW (2003): L-Dopa medication remediates cognitive inflexibility, but increases impulsivity in
patients with Parkinson’s disease. Neuropsychologia 41:1431–1441.
14. Wasylyshyn C, Verhaeghen P, Sliwinski MJ (2011): Aging and task
switching: A meta-analysis. Psychol Aging 26:15–20.
15. Oades RD (1985): The role of noradrenaline in tuning and dopamine
in switching between signals in the CNS. Neurosci Biobehav Rev 9:
261–282.
16. Evenden JL, Robbins TW (1985): The effects of d-amphetamine,
chlordiazepoxide and alpha-flupenthixol on food-reinforced tracking
of a visual stimulus by rats. Psychopharmacology 85:361–366.
17. Crofts HS, Dalley JW, Collins P, Van Denderen JC, Everitt BJ, Robbins
TW, et al. (2001): Differential effects of 6-OHDA lesions of the frontal
cortex and caudate nucleus on the ability to acquire an attentional
set. Cereb Cortex 11:1015–1026.
18. Mehta MA, Manes FF, Magnolfi G, Sahakian BJ, Robbins TW (2004):
Impaired set-shifting and dissociable effects on tests of spatial
working memory following the dopamine D2 receptor antagonist
sulpiride in human volunteers. Psychopharmacology 176:331–342.
19. McCabe SE, Knight JR, Teter CJ, Wechsler H (2005): Non-medical use
of prescription stimulants among US college students: Prevalence and
correlates from a national survey. Addiction 100:96–106.
20. van Holstein M, Aarts E, van der Schaaf ME, Geurts DEM, Verkes RJ,
Franke B, et al. (2011): Human cognitive flexibility depends on
dopamine D2 receptor signaling. Psychopharmacology 218:567–578.
21. Colzato LS, Waszak F, Nieuwenhuis S, Posthuma D, Hommel B (2010):
The flexible mind is associated with the catechol-O-methyltransferase
(COMT) Val158Met polymorphism: Evidence for a role of dopamine in
the control of task-switching. Neuropsychologia 48:2764–2768.
22. Stelzel C, Basten U, Montag C, Reuter M, Fiebach CJ (2010):
Frontostriatal involvement in task switching depends on genetic
differences in d2 receptor density. J Neurosci 30:14205–14212.
23. Aarts E, Roelofs A, Franke B, Rijpkema M, Fernández G, Helmich RC,
et al. (2010): Striatal dopamine mediates the interface between
motivational and cognitive control in humans: Evidence from genetic
imaging. Neuropsychopharmacology 35:1943–1951.
24. Rogers R, Monsell S (1995): Costs of a predictable switch between
simple cognitive tasks. J Exp Psychol Gen 124:207–231.
25. Riccardi P, Li R, Ansari MS, Zald D, Park S, Dawant B, et al. (2006):
Amphetamine-induced displacement of [18F] fallypride in striatum
and extrastriatal regions in humans. Neuropsychopharmacology 31:
1016–1026.
26. Zald DH, Woodward ND, Cowan RL, Riccardi P, Ansari MS, Baldwin RM,
et al. (2010): The interrelationship of dopamine D2-like receptor
availability in striatal and extrastriatal brain regions in healthy
humans: A principal component analysis of [18F]fallypride binding.
Neuroimage 51:53–62.
27. Christian BT, Narayanan T, Shi B, Morris ED, Mantil J, Mukherjee J
(2004): Measuring the in vivo binding parameters of [18F]-fallypride in
monkeys using a PET multiple-injection protocol. J Cereb Blood Flow
Metab 24:309–322.
28. Buckholtz JW, Treadway MT, Cowan RL, Woodward ND, Li R, Ansari
MS, et al. (2010): Dopaminergic network differences in human
impulsivity. Science 329:532.
29. Buckholtz JW, Treadway MT, Cowan RL, Woodward ND, Benning SD,
Li R, et al. (2010): Mesolimbic dopamine reward system hypersensitivity in individuals with psychopathic traits. Nat Neurosci 13:419–421.
30. Kessler RM (1995): Imaging of Cerebral Dopamine D2 Receptors; IND
application 47,245.
31. Lammertsma AA, Bench CJ, Hume SP, Osman S, Gunn K, Brooks DJ,
et al. (1996): Comparison of methods for analysis of clinical
[11C]raclopride studies. J Cereb Blood Flow Metab 16:42–52.
BIOL PSYCHIATRY ]]]];]:]]]–]]] 7
32. Hall H, Sedvall G, Magnusson O, Kopp J, Halldin C, Farde L (1994):
Distribution of D1- and D2-dopamine receptors, and dopamine and
its metabolites in the human brain. Neuropsychopharmacology 11:
245–256.
33. Kessler RM, Mason NS, Jones C, Ansari MS, Manning RF, Price RR
(2000): [18F]N-allyl-5-fluoropropylepidepride (fallypride): Radiation
dosimetry, quantification of striatal and extrastriatal dopamine receptors in man. Neuroimage 11:S32.
34. Seamans JK, Yang CR (2004): The principal features and mechanisms
of dopamine modulation in the prefrontal cortex. Prog Neurobiol 74:
1–58.
35. Wang M, Vijayraghavan S, Goldman-Rakic PS (2004): Selective D2
receptor actions on the functional circuitry of working memory.
Science 303:853–856.
36. Cools R, D’Esposito M (2011): Inverted-U-shaped dopamine actions on
human working memory and cognitive control. Biol Psychiatry 69:
e113–e125.
37. Mattay VS, Goldberg TE, Fera F, Hariri AR, Tessitore A, Egan MF, et al.
(2003): Catechol O-methyltransferase val158-met genotype and individual variation in the brain response to amphetamine. PNAS 100:
6186–6191.
38. Cools R (2011): Dopaminergic control of the striatum for high-level
cognition. Curr Opin Neurobiol 21:402–407.
39. van Schouwenburg MR, Ouden den HEM, Cools R (2010): The human
basal ganglia modulate frontal-posterior connectivity during attention shifting. J Neurosci 30:9910–9918.
40. McNab F, Klingberg T (2008): Prefrontal cortex and basal ganglia
control access to working memory. Nat Neurosci 11:103–107.
41. Berridge CW, Stalnaker TA (2002): Relationship between low-dose
amphetamine-induced arousal and extracellular norepinephrine and
dopamine levels within prefrontal cortex. Synapse 46:140–149.
42. Clatworthy PL, Lewis SJG, Brichard L, Hong YT, Izquierdo D, Clark L,
et al. (2009): Dopamine release in dissociable striatal subregions
predicts the different effects of oral methylphenidate on reversal
learning and spatial working memory. J Neurosci 29:4690–4696.
43. Cools R, Frank MJ, Gibbs SE, Miyakawa A, Jagust WJ, D’Esposito M
(2009): Striatal dopamine predicts outcome-specific reversal learning
and its sensitivity to dopaminergic drug administration. J Neurosci 29:
1538–1543.
44. Haber SN (2003): The primate basal ganglia: Parallel and integrative
networks. J Chem Neuroanat 26:317–330.
45. Alexander GE, Crutcher MD, DeLong MR (1990): Basal gangliathalamocortical circuits: Parallel substrates for motor, oculomotor,
“prefrontal” and “limbic” functions. Prog Brain Res 85:119–146.
46. Mink JW (1996): The basal ganglia: Focused selection and inhibition of
competing motor programs. Prog Neurobiol 50:381–425.
47. Sánchez-González MA, Garcı́a-Cabezas MA, Rico B, Cavada C (2005):
The primate thalamus is a key target for brain dopamine. J Neurosci
25:6076–6083.
48. Garcı́a-Cabezas MA, Martı́nez-Sánchez P, Sánchez-González MA, Garzón M, Cavada C (2009): Dopamine innervation in the thalamus:
Monkey versus rat. Cereb Cortex 19:424–434.
49. Advokat C (2010): What are the cognitive effects of stimulant
medications? Emphasis on adults with attention-deficit/hyperactivity
disorder (ADHD). Neurosci Biobehav Rev 34:1256–1266.
50. Hermens DF, Cooper NJ, Kohn M, Clarke S, Gordon E (2005):
Predicting stimulant medication response in ADHD: Evidence from
an integrated profile of neuropsychological, psychophysiological and
clinical factors. J Integr Neurosci 4:107–121.
51. Featherstone RE, Rizos Z, Kapur S, Fletcher PJ (2008): A sensitizing
regimen of amphetamine that disrupts attentional set-shifting does
not disrupt working or long-term memory. Behav Brain Res 189:
170–179.
52. Swanson JM, Volkow ND (2008): Increasing use of stimulants warns of
potential abuse. Nature 453:586.
53. Greely H, Sahakian B, Harris J, Kessler RC, Gazzaniga M, Campbell P,
et al. (2008): Towards responsible use of cognitive-enhancing drugs
by the healthy. Nature 456:702–705.
54. Maher B (2008): Poll results: Look who’s doping. Nature 452:647–675.
55. Lambert NM, McLeod M, Schenk S (2006): Subjective responses to
initial experience with cocaine: An exploration of the incentivesensitization theory of drug abuse. Addiction 101:713–725.
www.sobp.org/journal